Please use this identifier to cite or link to this item:
Scopus Web of Science® Altmetric
Type: Conference paper
Title: Visual distance measures for object retrieval
Author: Chen, Y.
Dick, A.
Li, X.
Citation: Proceedings of the International Conference on Digital Image Computing Techniques and Applications, held in Fremantle, 3-5 December, 2012: pp.1-8
Publisher: IEEE
Publisher Place: USA
Issue Date: 2012
ISBN: 9781467321792
Conference Name: International Conference on Digital Image Computing Techniques and Applications (2012 : Fremantle)
Statement of
Yanzhi Chen, Anthony Dick and Xi Li
Abstract: This paper describes an enhanced visual distance measure for image features, and evaluates its effect on object retrieval accuracy for several standard datasets. The measure incorporates semantic proximity information that is automatically extracted from each dataset in an offline step. It is designed to overcome errors introduced by feature detection and quantization in the “bag-of-words” model. We define a cross-word image similarity measure using this visual word distance, and show that it improves object retrieval precision for several datasets. It involves minimal additional query time cost, and can be embedded into any object retrieval method that uses a “bag-of-words” model.
Keywords: Atmospheric measurements; buildings; particle measurements; semantics; standards; vectors; visualisation
Rights: © 2012 IEEE
RMID: 0020126400
DOI: 10.1109/DICTA.2012.6411668
Appears in Collections:Computer Science publications

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.